Find Best Preprocessing Steps During Model Selection We have to be careful to properly handle preprocessing when conducting model selection. First, GridSearchCV uses cross-validation to determine which model has the highest performance. However, in cross-validation we are in effect pretending that the fold held out as the test set is not seen, and thus not part of …
Recall Preliminaries /* Load libraries */ from sklearn.model_selection import cross_val_score from sklearn.linear_model import LogisticRegression from sklearn.datasets import make_classification Generate Features And Target Data /* Generate features matrix and target vector */ X, y = make_classification(n_samples = 10000, n_features = 3, n_informative = 3, n_redundant = 0, n_classes = 2, random_state = 1) Create Logistic Regression …
Precision Preliminaries /* Load libraries */ from sklearn.model_selection import cross_val_score from sklearn.linear_model import LogisticRegression from sklearn.datasets import make_classification Generate Features And Target Data /* Generate features matrix and target vector */ X, y = make_classification(n_samples = 10000, n_features = 3, n_informative = 3, n_redundant = 0, n_classes = 2, random_state = 1) Create Logistic Regression …
Plot The Validation Curve Preliminaries /* Load libraries */ import matplotlib.pyplot as plt import numpy as np from sklearn.datasets import load_digits from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import validation_curve Load Digits Dataset /* Load data */ digits = load_digits() /* Create feature matrix and target vector */ X, y = digits.data, digits.target Plot Validation …
Plot The Receiving Operating Characteristic Curve Preliminaries /* Load libraries */ from sklearn.datasets import make_classification from sklearn.linear_model import LogisticRegression from sklearn.metrics import roc_curve, roc_auc_score from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt Generate Features And Target /* Create feature matrix and target vector */ X, y = make_classification(n_samples=10000, n_features=10, n_classes=2, n_informative=3, random_state=3) Split Data Intro …
Plot The Learning Curve Preliminaries /* Load libraries */ import numpy as np import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import load_digits from sklearn.model_selection import learning_curve Load Digits Dataset /* Load data */ digits = load_digits() /* Create feature matrix and target vector */ X, y = digits.data, digits.target Plot Learning …
Generate Text Reports On Performance Preliminaries /* Load libraries /* from sklearn import datasets from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report Load Iris Flower Data /* Load data */ iris = datasets.load_iris() /* Create feature matrix */ X = iris.data /* Create target vector */ y = iris.target /* Create …
F1 Score Preliminaries /* Load libraries */ from sklearn.model_selection import cross_val_score from sklearn.linear_model import LogisticRegression from sklearn.datasets import make_classification Generate Features And Target Data /* Generate features matrix and target vector */ X, y = make_classification(n_samples = 10000, n_features = 3, n_informative = 3, n_redundant = 0, n_classes = 2, random_state = 1) Create Logistic …
Custom Performance Metric Preliminaries /* Load libraries */ from sklearn.metrics import make_scorer, r2_score from sklearn.model_selection import train_test_split from sklearn.linear_model import Ridge from sklearn.datasets import make_regression Create Feature /* Generate features matrix and target vector */ X, y = make_regression(n_samples = 100, n_features = 3, random_state = 1) /* Create training set and test set */ …
Cross Validation With Parameter Tuning Using Grid Search In machine learning, two tasks are commonly done at the same time in data pipelines: cross validation and (hyper)parameter tuning. Cross validation is the process of training learners using one set of data and testing it using a different set. Parameter tuning is the process to selecting …